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(Week 1) Discrete Optimization

Week 1NP completePolynomial time solution checkingSolving 1 NP complete problem solves all other NP complete problemsReduction of 1 problem to anotherIn practice, we solve small problems, "push the exponential", so we can solve the practical problems Examples Kidney Exchanges Pair of D / R (Doner, Receiver), that are not compatible Week 2ModelingDefine precise description of the problem that everyone can agree uponInputGoal (What to optimize) Constraints StepsChoose decision variablesSomething that captures the real decisions you're interested in Express the problem constraints in terms of the variablesSpecify the solutions to the problemExpress the objective function Specify quality of each solution KnapsackDecision ProblemXi = 1 : Item is selectedXi = 0 : Item is not selected Constraint\sum_{i} w_i x_i \leq KObjective Function \sum_{i} v_i x_iMaximize objective subject to constraint What's left? Find values for the decision variablesNumber of solutions : 2^|I| ; t…

(Coursera) Week 6 of Spatial Data Science and Applications

6.1 Desktop GIS
Limitations Simple spatial analysis of demand & supply gives an insight of timber land investment Assumption for no activities over boundaries
6.2 Server GIS  Use caseIntegrated municipal spatial databasesWhen is Server GIS appropriate?Multiple actors with different desired rolesData is managed at the central DBMS, with DBMS features WorkflowSet SDBMSPostgresSQL, PostGISCreate UsersSet PrivilegeUpload datasetView & Analyze data using QGIS https://learnosm.org/it/osm-data/setting-up-postgresql/6.3 Spatial Data Analytics I Spatial DependencyMOHW (Ministry of Health and Welfare) wants to check any spatial relationships between districts and disease prevalence rate. Influential Variable DetectionMOHW also wnat to see if there's any regional factors that influence disease prevalence rateSolutionsFirst stage: Spatial Autocorrelation Analysis Conducted with respect to disease prevalence rate of adminstrative districtFinds the list of diseases with spatial autocorrelat…

(Coursera) Week 5 of Spatial Data Science and Applications

https://www.coursera.org/learn/spatial-data-science

Week 5 seemed most interesting since it covers some of math and science not 

5.1 Introduction


What is spatial data analyticsScience of processing spatial data with the goal of discovering useful informationCategorization (based on input data and outcome) of Spatial Data Analysis Measurement & Basic GeoProcessing Proximity and Accessibility AnalysisSpatial AutocorrelationSpatial Interpolation Spatial Categorization Clusteirng & HotspotFactor AnalysisTerrain AnalysisNetwork AnalysisSpatio-temporal data mining Example: Trajectory analysis 
5.1 Proximity and AccessibilityGoal : Determine the distance relationship between selected feature and other features Demand vs. SupplyDemand: What's the closest store from my placeSupply : What's the area that the place can reach to Thiessen Polygons / Voronoi diagram Partitioning of plane with input points into polygonsEach polygon contains exactly 1 point Boundaries define the area that…

Neil Greshams Masterclass Summary

Number of days I spent snowboarding (18/19 season)

Summary: Bought the epic pass and made a pretty good use of it

Days

December (7)
- (12/07) : Whistler
- (12/08) : Whistler
- (12/09) : Whistler
- (12/16) : Stevens
- (12/20) : Stevens, lesson
- (12/23) : Stevens
- (12/30) : Stevens

January (7)
- (01/06) : Stevens
- (01/10) : Park City
- (01/11) : Park City
- (01/12) : Park City
- (01/13) : Park City
- (01/20) : Stevens
- (01/27) : Stevens, lesson

February (7)
- (02/03) : Stevens
- (02/13) : Breckenridge
- (02/14) : Breckenridge - (02/15) : Keystone
- (02/16) : Breckenridge - (02/17) : Breckenridge - (02/23) : Stevens
March (7) - (03/02) : Stevens - (03/08) : Whistler - (03/09) : Whistler - (03/10) : Whistler - (03/15) : Crystal - (03/30) : Whistler - (03/31) : Whistler

April ?  - at least 2 more days so we can hit 30 :D

Reading "Transactional Storage for geo-replicated systems"

Abstract


Design and implementation of Walter - KV Store that supports TX, and replicates data across sitesParallel Snapshot isolation (PSI)allows asynchronous data replicationstrong guarantees within each siteprecludes WW conflictsWalter uses preferred sites and counting sets to implement PSI


Introduction


Geo-replicated = across many sitesClaimStrong consistency within site is desirable (e.g. user sees their own R/W in their site in the correct order) but weaker consistency is OK across sites (e.g. user can tolerate delay for their R/W to be seen by others). Asynchronous replication provides properties of weaker consistencyPSI provides just thisconsistent snapshot within a sitecausal ordering (not global) across sites w/o WW conflicts Walter uses two techniquespreferred sites counting set PSI performancequickly commit tx that write objects at their preferred sites or use csetsothers - resort to TPCOverview
Walter's 4 featuresAsynchronous replication across sitesUpdate-anywhere for cou…

(08/13/2018) Mt. Rainier Summit

Image
14k ft over 3 days

Next up
(2019) expeditions seminar north cascades
(2020) aconcagua
(2021) peru
(2020) denali